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Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain
Chi Zhang1,2, Fengyu Cong1,2, Tuomo Kujala2
1School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces a network entropy (NE) method to analyze dynamic brain connectivity, effectively reducing noise from spurious interactions. The new approach accurately tracks cognitive events, showing promise for brain function studies and state detection.
Area of Science:
- Neuroscience
- Cognitive Science
- Data Science
Background:
- Dynamic functional brain networks are crucial for understanding cognitive processes.
- Heterogeneity in functional connectivity graphs of the brain (FCGB) due to spurious interactions complicates the analysis of dynamic changes.
- Existing methods struggle to accurately capture real-time interactive information during complex tasks.
Purpose of the Study:
- To develop a novel network entropy (NE) method for measuring connectivity uncertainty in FCGB sequences.
- To address the challenge of spurious interactions in dynamic network analysis.
- To analyze event-related changes in brain activity during complex cognitive tasks, specifically driving.
Main Methods:
- Calculated adjacency matrices from electroencephalogram (EEG) data using a sliding time-window to form FCGB sequences.
- Replaced traditional Shannon entropy with a connection sequence distribution to quantify FCGB uncertainty for NE calculation.
- Applied time-frequency transform to NE of FCGB sequences for analyzing single-trial, event-related oscillatory activity.
Main Results:
- The proposed NE method demonstrated time-locked performance for events related to driver fatigue during prolonged driving.
- Time errors between detected high-power NE events and actual event occurrences were within [-30s, 30s], with 90.1% within [-10s, 10s].
- A high correlation (r = 0.99997, p < 0.001) was found between NE timing characteristics and recorded event times, validating NE's ability to reflect dynamic brain interactions.
Conclusions:
- The network entropy method effectively quantifies connectivity uncertainty in dynamic brain networks, mitigating issues from spurious interactions.
- The NE method shows significant potential for real-time monitoring of cognitive states and physiological changes during complex tasks.
- This approach offers a valuable tool for advancing cognitive neuroscience research and developing practical applications for state detection.
Keywords:
brain networkconnectivitydriver fatiguedynamic network analysisevent-related analysisnetwork entropyMore Related Videos
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